Title :
Identification of Protein Coding Regions Using the Modified Gabor-Wavelet Transform
Author :
Mena-Chalco, Jesús P. ; Carrer, Helaine ; Zana, Yossi ; Cesar, Roberto M., Jr.
Author_Institution :
Dept. de Cienc. da Comput., Inst. de Matemdtica e Estahstica da Univ. de Sao Paulo, Sao Paulo
Abstract :
An important topic in genomic sequence analysis is the identification of protein coding regions. In this context, several coding DNA model-independent methods based on the occurrence of specific patterns of nucleotides at coding regions have been proposed. Nonetheless, these methods have not been completely suitable due to their dependence on an empirically predefined window length required for a local analysis of a DNA region. We introduce a method based on a modified Gabor-wavelet transform (MGWT) for the identification of protein coding regions. This novel transform is tuned to analyze periodic signal components and presents the advantage of being independent of the window length. We compared the performance of the MGWT with other methods by using eukaryote data sets. The results show that MGWT outperforms all assessed model-independent methods with respect to identification accuracy. These results indicate that the source of at least part of the identification errors produced by the previous methods is the fixed working scale. The new method not only avoids this source of errors but also makes a tool available for detailed exploration of the nucleotide occurrence.
Keywords :
DNA; biology computing; cellular biophysics; genetics; molecular biophysics; pattern recognition; proteins; signal processing; wavelet transforms; DNA region; empirically predefined window length; eukaryote data sets; genomic sequence analysis; modified Gabor-wavelet transform; nucleotides patterns; pattern recognition; periodic signal components; protein coding region identification; Biology and genetics; Pattern Recognition; Signal processing; Computational Biology; DNA; Databases, Nucleic Acid; Databases, Protein; Globins; Humans; Models, Statistical; Pattern Recognition, Automated; Proteins; Sequence Analysis, DNA; Signal Processing, Computer-Assisted;
Journal_Title :
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
DOI :
10.1109/TCBB.2007.70259